In the medical imaging community, researchers have shown that the formation of smoke and the lighting condition for laparoscopic surgery images are very different, which limits the “in the wild" smoke detection methodologies applicability in medical domain [1]}, [2]}. Recently, Chou et al. [3]} explore temporal differences information that is motion blur and block analysis between current and previous frames as features for smoke detection. Loukas et al. [4]}, propose a method to detect electrocautery smoke for surgical events retrieval. They extract different features from optical flow estimated by Kanade-Lucas-Tomasi (KLT) algorithm [5]}, and then a support vector machine (SVM) is trained to classify each shot. Leibetseder et al. in [1]}, [2]} propose saturation histogram based classification methodologies Saturation Peak Analysis (SPA) and Saturation Analysis (SAN) and deep learning (DL) techniques. In [2]}, Leibetseder et al. train three variants of convolutional neural network (CNN) models: GoogleLeNet [9]}
trained with RGB images (GLN RGB) and saturation channels only (GLN SAT), and a modified AlexNet [10]} trained on RGB images (ALEX RGB). The performance of their CNN models on Cholec80 [11]} dataset are very similar to that of SAN or SPA. While DL shows its promising performance on smoke classification [1]}, [2]}, to the best of our knowledge there is no work that does image enhancement for smoke classification. Therefore, we limit to comparisons with the non deep learning based methods only in our experimental section. Moreover, one can consistently improve the classification performance by employing our enhancement method and then train the CNN architectures, following the ideas proposed in Sharma et al. [14]}.
